1. Validate demand
Assess whether the bus travel category had enough organic search demand to justify a dedicated SEO vertical.
Data Analytics Case Study
A data-driven case study using keyword, intent and competitor-ranking data to assess whether a travel company should launch a new organic bus travel vertical across Spain, France and the UK.
This project evaluates a new organic growth opportunity using a keyword research dataset and a competitor-ranking sample. The goal was to move from raw SEO data to a clear business recommendation: which market to enter first, what type of page to prioritise and where the most accessible opportunity was located.
Which market should be prioritised first for a new organic bus travel vertical, and which type of SEO page should anchor the launch strategy?
Assess whether the bus travel category had enough organic search demand to justify a dedicated SEO vertical.
Compare Spain, France and the UK using raw search volume, non-brand opportunity, intent mix and competitive accessibility.
Translate the data into a launch recommendation: first market, second market, selective pilots and page-type priority.
The project was based on keyword demand data and a competitor-ranking sample. For public portfolio use, the data was cleaned, anonymised and converted into a documented workbook.
| Dataset | Description | Key fields | Analytical purpose |
|---|---|---|---|
| Keyword Research | Bus-related keywords across Spain, France and the UK. | Keyword, market, search volume, CPC, competition and intent. | Demand analysis and market comparison. |
| Competitor Rankings | Sample of ranking competitor domains for bus-related searches. | Domain, keyword, estimated position, estimated traffic and URL. | Competitive landscape and SERP accessibility. |
| Clean Crosswalk | Joined analytical table created after cleaning and normalisation. | Keyword norm, market, intent, segment, brand flag and ranking signals. | Combined opportunity assessment. |
Description
Bus-related keywords across Spain, France and the UK.
Key fields
Keyword, market, search volume, CPC, competition and intent.
Purpose
Demand analysis and market comparison.
Description
Sample of ranking competitor domains for bus-related searches.
Key fields
Domain, keyword, estimated position, estimated traffic and URL.
Purpose
Competitive landscape and SERP accessibility.
Description
Joined analytical table created after cleaning and normalisation.
Key fields
Keyword norm, market, intent, segment, brand flag and ranking signals.
Purpose
Combined opportunity assessment.
The original dataset needed to be inspected before analysis. The cleaning process focused on making keywords comparable, standardising numeric fields and separating raw demand from addressable SEO opportunity.
| Data issue | Why it mattered | Cleaning action |
|---|---|---|
| Inconsistent CPC formatting | Decimal commas and decimal points could break numerical calculations. | Standardised CPC into a numeric field. |
| Duplicate keyword-market entries | Duplicates could inflate total demand and distort market comparison. | Created a duplicate flag and excluded duplicates from summary metrics. |
| Keyword spelling and accent variations | Variations could prevent correct matching between tables. | Created a normalised keyword field. |
| Brand and operator terms mixed with generic queries | Raw volume could overstate the accessible opportunity for a new entrant. | Created a brand vs non-brand classification. |
| Ranking data without complete market information | Competitor data could not be compared by market without inference. | Inferred market where possible and retained match status for transparency. |
| Different competitor types | Operators, OTAs and marketplaces represent different levels of competitive accessibility. | Classified competitor domains by player type. |
Standardised CPC into a numeric field so calculations would not break.
Created a duplicate flag and excluded duplicates from summary metrics.
Created a normalised keyword field to support matching and duplicate checks.
Separated brand/operator demand from non-brand demand to estimate accessible opportunity.
Inferred market where route, language or competitor domain made the market clear.
Classified competitor domains as Operator, Aggregator/OTA, Marketplace or Unclear.
The analysis compared markets using demand size, addressable non-brand opportunity, intent mix, query segment distribution and competitive structure.
Compares raw monthly search demand across Spain, France and the UK.
Shows how much of each market's demand is accessible beyond operator or brand-specific searches.
Compares transactional, navigational and informational demand across markets.
Classifies visible competitors as operators, aggregators/OTAs or marketplaces.
Identifies which query types matter most in each market: route, cheap tickets, airport transfers, schedules and other segments.
The analysis showed that raw volume and accessible SEO opportunity were not the same. Market prioritisation depended on demand quality, brand concentration and competitive accessibility.
The UK market showed high total search demand, but a larger share was concentrated around strong operator brands and entrenched competitors.
France combined meaningful non-brand demand with the highest accessible share, making it the strongest first launch candidate.
Spain showed strong route-led search demand and meaningful timetable or support intent that could be integrated into commercial route pages.
City-to-city route pages offered the best combination of search demand, commercial intent and scalable page architecture across the three markets.
The recommendation was based on the relationship between demand size, addressable opportunity and competitive accessibility.
Priority 1
France offered the strongest accessible opportunity because of its high non-brand share and clearer first-entry profile.
Priority 2
Spain had strong route-led demand and enough commercial search behaviour to support a second-stage rollout.
Priority 3
The UK showed the largest raw demand, but stronger operator dominance made a selective pilot more appropriate than a broad launch.
The core SEO strategy should be route-page-led rather than blog-first. City-to-city route pages should anchor the vertical, supported by country hubs and selected commercial pages such as cheap bus tickets where demand justifies it.
The original analysis can be reproduced with spreadsheet formulas and summary tables. The SQL examples below show how the same logic could be translated into a database environment.
SELECT
market,
SUM(avg_monthly_searches) AS total_search_volume,
COUNT(DISTINCT keyword_norm) AS unique_keywords
FROM keyword_clean
WHERE dup_keyword_market_flag = FALSE
GROUP BY market
ORDER BY total_search_volume DESC;
SELECT
market,
SUM(avg_monthly_searches) AS non_brand_search_volume,
COUNT(DISTINCT keyword_norm) AS non_brand_keywords
FROM keyword_clean
WHERE brand_flag = FALSE
AND dup_keyword_market_flag = FALSE
GROUP BY market
ORDER BY non_brand_search_volume DESC;
SELECT
market,
intent,
SUM(avg_monthly_searches) AS search_volume,
COUNT(DISTINCT keyword_norm) AS keyword_count
FROM keyword_clean
WHERE dup_keyword_market_flag = FALSE
GROUP BY market, intent
ORDER BY market, search_volume DESC;
SELECT
market,
query_segment,
SUM(avg_monthly_searches) AS segment_search_volume,
COUNT(DISTINCT keyword_norm) AS keyword_count
FROM keyword_clean
WHERE dup_keyword_market_flag = FALSE
GROUP BY market, query_segment
ORDER BY market, segment_search_volume DESC;
SELECT
market,
player_type,
COUNT(*) AS ranking_rows,
SUM(estimated_monthly_traffic) AS estimated_traffic,
MIN(estimated_position) AS best_position
FROM rankings_clean
WHERE market IS NOT NULL
AND market <> ''
GROUP BY market, player_type
ORDER BY market, estimated_traffic DESC;
Download the executive case summary or inspect the cleaned workbook with the documented data structure, summary tables, QA checks, data dictionary and SQL examples.
Cleaned and anonymised workbook with source data structure, formula-driven summary tables, QA checks, data dictionary and SQL examples.
Short executive summary of the analysis, including the business context, methodology, key findings, recommendation and limitations.